Linear and Non-linear Causality Test in a LSTAR model - wavelet decomposition in a non-linear environment

Yushu Li and Ghazi Shukur

No 227, Working Paper Series in Economics and Institutions of Innovation from Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies

Abstract: In this paper, we use simulated data to investigate the power of different causality tests in a two-dimensional vector autoregressive (VAR) model. The data are presented in a non-linear environment that is modelled using a logistic smooth transition autoregressive (LSTAR) function. We use both linear and non-linear causality tests to investigate the unidirection causality relationship and compare the power of these tests. The linear test is the commonly used Granger causality test. The non-linear test is a non-parametric test based on Baek and Brock (1992) and Hiemstra and Jones (1994). When implementing the non-linear test, we use separately the original data, the linear VAR filtered residuals, and the wavelet decomposed series based on wavelet multiresolution analysis (MRA). The VAR filtered residuals and the wavelet decomposition series are used to extract the non-linear structure of the original data. The simulation results show that the non-parametric test based on the wavelet decomposition series (which is a model free approach) has the highest power to explore the causality relationship in the non-linear models.

Keywords: Granger causality; LSTAR model; Wavelet multiresolution; Monte Carlo simulation (search for similar items in EconPapers)
JEL-codes: C01 C10 C51 C52 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2010-04-10
New Economics Papers: this item is included in nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:hhs:cesisp:0227

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